Prediction of Optimal Business Structure for Tax Efficiency

With the growing relevance of data, PwC has embraced Data & Analytics as part of its culture. The Tax practice, specifically, strives to bring value to market by empowering domain experts with automation and data-driven services. Among their initiatives, the Tax Technology team is investigating methods to evaluate the quality of corporate tax returns (T2). Given the complex nature of these documents, this problem requires exploring distinct issues:

1. A T2 can contain hundreds of sub-forms supported by large professional teams within distinct specializations. Scalable methods are needed to identify finalized tax returns among drafts and the large volume of work product produced by these teams.

2. A mapping between each field in a tax return and its corresponding sub-form does not exist.

3. Tax forms contain slips—copies of a tax question which must be answered for any number of relevant cases—yielding forms of vastly different structures.

With the University of Toronto’s support, PwC hopes to assess the integrity of its data and develop proof-of-concepts to optimize service delivery, along with identifying practices and procedures that will differentiate them in the marketplace.

Faculty Supervisor:

Nathan Taback

Student:

Partner:

PricewaterhouseCoopers (Toronto, ON)

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program: